Generating and displaying customer commitment framework data

ABSTRACT

Systems and methods for generating and displaying customer commitment framework data. Exemplary methods for determining the shareability of online content may include obtaining, via a digital intelligence system, customer experience data regarding any of a product, a brand, and customer responses for a first entity, as well as periodically calculating, via the digital intelligence system, customer commitment framework data from the customer experience data, and generating a customer commitment dashboard that comprises a graphical representation of the customer commitment framework data.

CROSS-REFERENCES TO RELATED APPLICATIONS

This Non-Provisional U.S. Patent Application is a continuation-in-partof U.S. patent application Ser. No. 13/587,789, filed on Aug. 16, 2012and titled “Customer Relevance Scores and Methods of Use”, now U.S. Pat.No. 8,793,154, issued on Jul. 29, 2014, which claims the prioritybenefit of U.S. Provisional Application No. 61/525,041, filed on Aug.18, 2011 and titled “New Sharing and Recommendation Tracking Method”.This application also relates to U.S. Provisional Patent Application No.61/675,784, filed on Jul. 25, 2012 and titled “Product Cycle AnalysisUsing Social Media Data”, as well as U.S. Provisional Patent ApplicationNo. 61/606,326, filed on Mar. 2, 2012 and titled “Product Cycle AnalysisUsing Social Media Data”. All of the aforementioned disclosures are allhereby incorporated by reference herein in their entireties includingall references cited therein.

FIELD OF THE PRESENT TECHNOLOGY

The present technology relates generally to generating and displayingcustomer commitment framework (CCF) data. Dashboards may be provided,which include representations of CCF data, such as graphs, tables, orother visual formats that provide users with views of CCF data in amanner that allows for real-time course-correction of programs andprocesses. Exemplary types of CCF data include product commitment scores(PCS), brand commitment scores (BCS), and customer response scores(CRS), which are each indicative of various aspects of a customerexperience and journey analysis.

BACKGROUND

Social media communications provide a wealth of information regardingthe purchasing behaviors and interests of consumers. While thisinformation is voluminous, it is often difficult to categorize andtranslate this information into meaningful and actionable informationthat may be utilized by a company to improve their products,advertising, customer service, and the like.

SUMMARY OF THE DISCLOSURE

According to some embodiments, the present technology may be directed toa method for determining the shareability of online content. The methodmay comprise: (a) obtaining, via a digital intelligence system, customerexperience data regarding any of a product, a brand, and customerrelevance for a first entity; (b) periodically calculating, via thedigital intelligence system, customer commitment framework data from thecustomer experience data; and (c) generating a customer commitmentdashboard that comprises a graphical representation of the customercommitment framework data.

According to other embodiments, the present technology may be directedto systems for determining the shareability of online content. Thesesystems may comprise: (a) one or more processors; and (b) logic encodedin one or more tangible media for execution by the one or moreprocessors and when executed operable to perform operations comprising:(i) obtaining, via a digital intelligence system, customer experiencedata regarding any of a product, a brand, and customer relevance for afirst entity; (ii) periodically calculating, via the digitalintelligence system, customer commitment framework data from thecustomer experience data; and (iii) generating a customer commitmentdashboard that comprises a graphical representation of the customercommitment framework data.

According to additional embodiments, the present technology may bedirected to non-transitory computer readable storage mediums having acomputer program embodied thereon. The computer program is executable bya processor in a computing system to perform a method that includes thesteps of: (a) obtaining, via a digital intelligence system, customerexperience data regarding a first entity; (b) periodically calculating,via the digital intelligence system, customer commitment framework datafrom the customer experience data; and (c) generating a customercommitment dashboard that comprises a graphical representation of thecustomer commitment framework data.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain embodiments of the present technology are illustrated by theaccompanying figures. It will be understood that the figures are notnecessarily to scale and that details not necessary for an understandingof the technology or that render other details difficult to perceive maybe omitted. It will be understood that the technology is not necessarilylimited to the particular embodiments illustrated herein.

FIG. 1 is a block diagram of an exemplary digital intelligence system.

FIG. 2 is a block diagram of an exemplary digital intelligenceapplication for use in accordance with the present technology;

FIG. 3 illustrates exemplary keyword matrices for categorizing socialmedia conversations;

FIG. 4 is an exemplary customer commitment dashboard illustratingproduct commitment score data;

FIG. 5 is an exemplary customer commitment dashboard illustratingproduct commitment score data as well as brand commitment score data;

FIG. 6 is an exemplary customer commitment dashboard illustratingproduct commitment score data as well as brand commitment score data andcustomer relevancy score data;

FIG. 7 is an exemplary customer commitment dashboard illustratingproduct commitment score (PCS) data displayed according to segmentation;

FIG. 8 is an exemplary customer commitment dashboard illustrating PCSsegmentation and customer journey data;

FIG. 9 is an exemplary customer commitment dashboard illustrating PCSsegmentation and segmentation characteristics;

FIG. 10 is an exemplary customer commitment dashboard illustrating atime-based and graphical PCS data analysis;

FIG. 11 is an exemplary customer commitment dashboard illustrating atime-based and graphical analysis of customer journey data;

FIG. 12 is an exemplary customer commitment dashboard illustrating atime-based and graphical analysis of experience scores relative toproduct awareness; and

FIG. 13 is a block diagram of an exemplary computing system forimplementing embodiments of the present technology.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

While this technology is susceptible of embodiment in many differentforms, there is shown in the drawings and will herein be described indetail several specific embodiments with the understanding that thepresent disclosure is to be considered as an exemplification of theprinciples of the technology and is not intended to limit the technologyto the embodiments illustrated.

It will be understood that like or analogous elements and/or components,referred to herein, may be identified throughout the drawings with likereference characters. It will be further understood that several of thefigures are merely schematic representations of the present technology.As such, some of the components may have been distorted from theiractual scale for pictorial clarity.

Generally speaking, the present technology is directed to systems,methods, and media to generate and display customer commitment frameworkdata. Broadly, the customer commitment framework (CCF) empowersorganizations to optimize their customer experience with a data-drivenapproach to decision-making. CCF provides real-time predictive measuresand robust insights to strengthen customer commitment around the threeexemplary customer journeys, including Shopping, Sharing, and Advocacy.

The present technology provides intuitive dashboards, known as customercommitment dashboards (CCDs), which may advantageously be used to drivereal-time course-correction of programs and processes. Because CCDmodels CCF data, which includes customer experience and customer journeydata, product and brand scores may be benchmarked and compared tocompetitors, as well as an industry average, providing the ability toleverage best practices in any given industry vertical.

The present technology may calculate the aforementioned product andbrand scores that indicate how well received a product or brand isamongst consumers. These scores may also be used to predict buyingbehaviors and target consumers based upon their position within aproduct cycle. That is, scores may be calculated that represent consumerexperiences across many phases of a product cycle (e.g., development,launch, updating, phase out, and the like).

The terms “customer experience data” as used throughout this documentrefers to customer experience and/or customer journey data gathered froma wide variety of source including, but not limited to data gatheredfrom social networking platforms, as well as news sources, forums,blogs, and so forth. One of ordinary skill in the art will appreciatethat any digital data source that provides customer experience orcustomer journey data, as described herein, may likewise be utilized inaccordance with the present disclosure. An “author” may include anycustomer or potential customer that has generated content that expressesan opinion about a product or service, especially content that relatesto a customer experience or a customer journey.

Many of the examples provided herein reference the analysis ofconversations and content occurring via social media platforms. It willbe understood that these examples are non-limiting and the CCF analysismethods described herein may be equally applied to content occurring viaother content sources, such as those mentioned above.

Additionally, the terms “a product” may be claimed or referred to asboth products and/or services provide by an entity.

Generally, CCF data may be determined from the gathered customerexperience data. In some instances, the CCF data may include variousscores, such as PCS, BCS, and/or CRS, which are real-time predictivemeasures data, which may be used as a means to strengthen customercommitment.

Prior to calculating various scores that indicate how well received aproduct, customer experience, and/or customer journey is amongstconsumers, the present technology may evaluate customer experience datafrom authors and categorize conversations or other content. In someinstances, conversations may be categorized as falling within a productcommitment score domain, a brand commitment score domain, and/or acustomer relevance score domain. Generally speaking, conversations orcontent may be categorized by evaluating keywords included in theconversations, and more specifically based upon a frequency of keywordsincluded in these conversations.

In some instances, BCS may be utilized to determine a consumer'semotional connection to a brand. Exemplary emotions associated with theBCS may comprise, but are not limited to, hopefulness, attraction,and/or devotion. These emotions may be tied to segments of the productcycle, such as understand, learn, and commit—customer engagement levelswith a particular brand.

The CRS allows marketers to determine the shareability of content. Bymaking content more shareable, marketers can increase website traffic,which may in turn, drive consumer behaviors within the product cycle,such as buying. The CRS may quantify the shareability of content and maybe used as a benchmark for comparing the effectiveness of content indriving commercial activity.

Customer relevance scores (CRS) may quantify how likely it is that acustomer or social network user will share a particular piece ofcontent, such as a video, an article, a website, or other online contentwithin the context of a social network. The CRS gauges the shareabilityof content. Because it can be demonstrated with empirical evidence thatshared content increases revenue more than passive or unshared content,increasing the CRS for content may result in a corresponding increase inrevenue attributable to the content. In sum, the CRS may be used toquantify the value of the content, based upon its shareability.

Generally speaking, the present technology may be utilized to determinethe shareability of online content. Additionally, the present technologymay be utilized to evaluate how and why content is shared. Content thatis more frequently shared may be analyzed to determine various elementsthat make the content shareable, such as narrative, thematic, andunderlying message elements. Additionally, the present technology may beused to create code frames from frequently shared content. These codeframes (similar to templates) may be applied to other content toincrease the shareability of the content. Shareable content may becreated from scratch using these code frames.

Advantageously, the present technology may track customer experiencedata for online content, such as sharing of the content, downloading ofthe content, uploading of the content, conversations relating to thecontent, and so forth. Using this customer experience data, the presenttechnology may determine the shareability of the content. When contentsharing is quantified, the metrics gathered may be utilized to increasekey brand metrics, understand and quantify what makes certain contentshareable, increase advertising recall, increase correct branding ofadvertising, increase brand consideration, increase brandrecommendation, increase brand favorability, and increase enhancedresonance and acceptance of campaign messages.

In some instances, the present technology may mathematically quantifyconsumer sentiment relative to a product. Moreover, the consumersentiment may be extracted from an analysis of content included insocial media messages and conversations. Additionally, the portion ofthe product cycle in which the consumer is currently participating maybe determined by an analysis of the words included in their customerexperience data. Therefore, consumer sentiment regarding a product maybe determined relative to a time frame associated with at least onephase of a product cycle for the product.

In some exemplary embodiments, the scores calculated by the presenttechnology may be based upon data included in social media messages ofauthors (e.g., consumers posting messages on social networks). Thus,customer experience data obtained from various social media sources mayprovide valuable and actionable information when transformed by thepresent technology into various metrics. Each of themetrics/scores/values calculated by the present technology is describedin greater detail herein.

Referring to FIG. 1, the present technology may be implemented tocollect and evaluate customer experience data for analysis as customercommitment framework data. The present technology may be facilitated bya digital intelligence system 100, hereinafter “system 100” as shown inFIG. 1. The system 100 may be described as generally including one ormore web servers that may communicatively couple with client devicessuch as end user computing systems. For the purposes of clarity, thesystem 100 is depicted as showing only one web server 105 and one clientdevice 110 that are communicatively coupled with one another via anetwork 115. Additionally, customer experience data gathered fromvarious sources (not depicted in FIG. 1) may be stored in database 120,along with various scores, values, and the corresponding data generatedby the web server 105, as will be discussed in greater detail below.

It is noteworthy to mention that the network 115 may include any one (orcombination) of private or public communications networks such as theInternet. The client device 110 may interact with the web server 105 viaa web based interface, or an application resident on the client device110, as will be discussed in greater detail herein.

According to some embodiments, the system 100 may include a cloud basedcomputing environment that collects, analyzes, and publishes datasets.In general, a cloud-based computing environment is a resource thattypically combines the computational power of a large grouping ofprocessors and/or that combines the storage capacity of a large groupingof computer memories or storage devices. For example, systems thatprovide a cloud resource may be utilized exclusively by their owners,such as Google™ or Yahoo!™; or such systems may be accessible to outsideusers who deploy applications within the computing infrastructure toobtain the benefit of large computational or storage resources.

In some embodiments according to the present technology, the cloud maybe formed, for example, by a network of web servers such as web server105 with each web server (or at least a plurality thereof) providingprocessor and/or storage resources. These servers may manage workloadsprovided by multiple users (e.g., cloud resource consumers or otherusers). Typically, each user places workload demands upon the cloud thatvary in real-time, sometimes dramatically. The nature and extent ofthese variations typically depend on the type of business associatedwith the user.

The system 100 may be generally described as a particular purposecomputing environment that includes executable instructions that areconfigured to generate and display customer commitment framework data.

In some embodiments, the web server 105 may include executableinstructions in the form of a digital intelligence application,hereinafter referred to as “application 200” that collects and evaluatescustomer experience data used in various customer commitment frameworkdata analyses. FIG. 2 illustrates and exemplary schematic diagram of theapplication 200.

The application 200 is shown as generally comprising an interface module205, a data gathering module 210, a Product Commitment Score (PCS)module 215, a consumer experience module 220, a segmentation module 225,a CRS module 235, and a code framing module 240. It is noteworthy thatthe application 200 may include additional modules, engines, orcomponents, and still fall within the scope of the present technology.As used herein, the term “module” may also refer to any of anapplication-specific integrated circuit (ASIC), an electronic circuit, aprocessor (shared, dedicated, or group) that executes one or moresoftware or firmware programs, a combinational logic circuit, and/orother suitable components that provide the described functionality. Inother embodiments, individual modules of the application 200 may includeseparately configured web servers.

Generally speaking, the interface module 205 may generate a plurality ofgraphical user interfaces that allow end users to interact with theapplication 200. These graphical user interfaces may allow end users toinput information that is utilized by the system 100 to capture andanalyze customer experience data. The information input by end users mayinclude product information for products they desire to evaluate, theproduct cycle or a portion of the product cycle of interest, the type ofconsumers or messages they desire to analyze, and so forth.

Initially, the data gathering module 210 may be executed to obtaincustomer experience data from one or more digital mediums, such aswebsites, blogs, social media platforms, and the like. End users mayestablish profiles that define what types of customer experience dataare to be gathered by the data gathering module 210. For example, asoftware developer may desire to gather customer experience dataregarding consumer sentiment for a particular application.

The data gathering module 210 may evaluate customer experience data forkeywords, groups of keywords, or search queries that are utilized tosearch for conversations or messages that include these keywords.

FIG. 3 illustrates various matrices that may be used to semioticallyevaluate conversations or other content. For example, if a conversationhas a predominate number of keywords that fall in the customer relevancescore (CRS) matrix, the conversation may be categorized as fallingwithin the CRS domain. Thus, a CRS equation may be utilized to calculatea CRS for the social media conversation, as will be discussed in greaterdetail.

Exemplary PCS core keywords are shown in matrix 305, while exemplary BCScore keywords are included in matrix 310. Exemplary CRS core keywordsare included in matrix 315, which includes column 320 of Interested,column 325 of Connected, and column 330 of Sharing. Each of thesecolumns may be associated with a shareability classification in someembodiments. Thus, keywords in a conversation may place the conversationinto one or more of these classifications, namely Interested, Connected,and/or Sharing, respectively.

For example, if a conversation included the words sharing and endorsing,which are included in the Sharing column 330, the conversation may beclassified within the Sharing classification. The conversation may beplaced into more than one classification if the system detects keywordspresent in the Interested or Connected columns. In some instances, theconversation may be classified by a predominance of classifying words inthe conversation. Thus, if the conversation includes a predominatenumber of Interested keywords, the conversation may be classified asInterested. In some embodiments, these classifications may also beweighted such that the inclusion of a predetermined number of Sharingkeywords automatically causes the conversation to be classified with theSharing classification, regardless of how many other Interested orConnected keywords are present in the conversation.

In accordance with the present disclosure, selection of customerexperience data may be influenced by the specific types of behaviorsthat a merchant is attempting to quantify. In other embodiments, thedata gather module 210 may analyze the customer experience data todetermine where within the product cycle a consumer currentlyresides—for example, in the awareness, interest, desire, or actionphases. Awareness may be inferred from conversations that discuss any ofthe three key drivers of the product cycle (e.g., learn, try, buy, andthe like). Interest in a product may be a strong indicator that aconsumer has gone beyond being simply aware of a product. When consumersexpressing a desire to purchase a product it may be inferred to be astrong indicator that consumers are considering a product for purchase.Additionally, when consumers indicate an active intent to purchase aproduct, it may be inferred that the consumer is strongly progressingalong the buying cycle.

In some embodiments according to the present technology, the datagathering module 210 may obtain customer experience data from specifictypes of consumers, and in additional embodiments, based upon where theconsumers are positioned within the product cycle, such as those withinthe research phase. That is, the customer experience data for a set ofconsumers may be monitored because they are actively researchingproducts to purchase.

The data gathering module 210 may utilize a conversation matrix toobtain relevant customer experience data. The data gathering module 210may employ the conversation matrix to search and capture relevantcustomer experience data from various digital platforms. The searchterms and matrices utilized by the data gathering module 210 may beupdated if the data gathering module 210 fails to obtain sufficientdata, or if the data that is obtained is inaccurate.

Product Commitment Scores

The PCS module 215 may be executed to calculate various types of PCSvalues that aid merchants in determining the commitment level ofconsumers to a particular product. Additionally, the PCS value may beutilized as a leading indicator that may be utilized to predict consumerbehavior relative to a particular product or service. For example, thePCS value may be used to predict how well a particular product will bereceived by consumers. Moreover, the PCS value may be used to predictthe likelihood that a product will be purchased and if consumers willremain committed to the product throughout the lifecycle of the product.In some non-limiting examples, if the product includes software, theproduct lifecycle may include conception, product launch, and eventualupgrade of the software by consumers.

In some embodiments, the PCS value may represent the difference betweenthe number of positive research, trial, and purchase messages and thenumber of negative research, trial and purchase messages. Again, thesemessages may include social media messages and may be obtained by thedata gathering module 210 from evaluating one or more social mediaplatforms. More specifically, these messages or conversations have beenpreviously categorized as belonging to, or being associated with, thePCS domain.

It is noteworthy that the PCS module 215 may calculate individual PCSvalues at a specific consumer (e.g., author) level. Adjustments andweighting of consumer level PCS values may also be performed by the PCSmodule 215.

For example, each consumer may contribute to the overall PCS value tothe degree of their relative authority. That is, the PCS module 215 mayaccount for a consumer's influence relative to the total influence ofall consumers having at least one research, trial, or purchaseconversation relative to a particular product.

The PCS module 215 may also adjust consumer level PCS values to accountfor each consumer's influence relative to the influence of all consumershaving at least one research, trial, or purchase message. That is, themore influential a consumer is, the more weight is attributed to theconsumer's conversations. Influence may be inferred because the consumerhas a large social network or because the consumer is an expert in theproduct field.

The overall PCS value may generally comprise one or more summationconsumer level PCS value. In additional embodiments the overall PCSvalue (and consumer level PCS values) may comprise a summation of, forexample, three different component values such as a research value, atrial value, and a purchase value, where each of these values may becalculated separately. These three values represent the phases of theproduct cycle.

In general, each of the three component values may each include asummation of seven different sentiment values. Conceptually, the sevensentiment values exist on a continuum where the first sentiment valueindicates a very negative sentiment, and the seventh sentiment valueindicates a very positive sentiment. The second through sixth sentimentsfall somewhere in between. The distributions of messages/conversationsalong the spectrum of sentiment values may indicate the success of theproduct at the different phases of the product cycle.

In some embodiments, messages that are the most positive (sentimentscore of seven, for example) may receive the most points, whereas theleast positive (sentiment score of five, for example) may receive theleast amount of positive points. The most negative conversations(sentiment score of one) may receive the greatest number of negativepoints. Conversations being the least negative (sentiment score of four)may receive the fewest negative points.

As mentioned briefly above, consumer level PCS may also be weighted. Forexample, a consumer having 100% most positive conversations in theresearch, trial and purchase categories should get the maximum score of100. As such, the weight for sentiment seven=100/3=+33.33.

Likewise, a consumer having 100% most negative conversation in theresearch, trial and purchase categories should get the minimumscore=−100. As such, the weight for sentiment 1=−100/3=−33.33.

Consumers that have less negative conversations than sentiment one, adecrease in penalization points of −33.33 may be seen, respecting theoriginal weighting. As consumers have less positive conversations theirreward points may be reduced to respect the original weighting.

In sum, the PCS module 215 may consider not only the aggregate number ofconversations in each phase of the product cycle, but the sentimentlevel associated with each conversation. Additionally, the sentiment foreach conversation may be weighted based upon consumer characteristics(e.g., mood, influence, etc.). Moreover, the conversations may furtherbe weighted by the authority level of the consumers associated with theconversations. The final PCS (either overall or consumer level) may thenbe indexed from zero to 100, for example, where 100 indicates that theproduct scores perfectly through the product cycle or at least one phaseof the product cycle.

The present technology may be adapted to adjust the consumer level andoverall PCS values based upon various factors. For example, a valuecalculated for the sentiment of a message may be adjusted for theconsumer's general mood, such as when it is known that the consumer isalways positive or almost always skeptical and/or negative. In otherinstances the PCS values may be adjusted based upon the importance of aparticular message to the sale of a product or service.

While many methods for calculating and weighting PCS have been disclosedone or ordinary skill in the art will appreciate that other algorithmsand weighting methodologies that may be utilized to quantify and predictconsumer sentiment and buying behaviors for product cycles are likewisecontemplated for use in accordance with the present technology.

PCS values may also be utilized to benchmark a particular productagainst a competing product. For example, a PCS value for a navigationsoftware application for a first merchant may be compared against a PCSvalue for similar navigation software from a competing merchant. The PCSvalue may provide actionable information that allows the first merchantto modify their marketing, consumer service, and/or product features toincrease their PCS value. It is noteworthy to mention that PCS valuesmay be generated for merchants at specific intervals, such as daily,weekly, monthly, or quarterly.

According to some embodiments, the consumer experience module 220 may beexecuted to evaluate portions of the consumer journey (e.g., productcycle) relative to a product. Generally speaking, consumer experiencevalues may comprise mathematical representations of customer experiencedata at specific point in time (or a specific time period) along theproduct cycle. In some instances, the consumer experience scores mayinclude the three PCS component values (e.g., research value, trialvalue, and purchase value) described above, but analyzed relative to aparticular time frame. Therefore, the consumer experience values may bedescribed as more granular and temporally focused portion of the PCS(either intermediate or overall).

Consumers may be previously identified by the data gathering module 210,for example, by identifying consumers in certain types of survey data.Various scores may be generated by the consumer experience module 220that represent different consumer experiences. These scores/values maybe utilized by merchants to improve their products and/or marketingcampaigns.

Using the consumer experiences scores, a merchant may explore moredetailed metrics regarding the touchpoints surrounding a product. Insome instances, the consumer experience scores may be generated byconducting a more detailed evaluation of customer experience datarelative to the calculation of a PCS. Therefore, the conversationmatrices employed by the data gathering module 210 may be modified(e.g., may include greater detail) to capture more specific portions ofdigital conversations/messages across phases of the product cycle. Theconsumer experience module 220 may also generate optimal consumerjourney models that enable merchants to plan effective productdevelopment and marketing strategies, while also allowing for coursecorrection when products or marketing fail to produce acceptableconsumer experiences.

According to some embodiments, the segmentation module 225 may beexecuted to determine and develop actionable priorities tailored tospecific consumer types. The segmentation module 225 may clusterconsumers based on a variety of factors using a segmentation model thatconsiders product cycle components and likelihood of purchasing aproduct. The segmentation module 225 may utilize the social datagathered by the data gathering module 210. Additionally, thesegmentation module 225 may generate feedback for consumer segments innear real-time, specifically for consumers that are the most (andalternatively the least) likely to purchase a particular product.

In some embodiments, utilizing the data gathering module 210, consumercustomer experience data may be obtained from groups of consumersengaged in traditional marketing or consumer research activities.Consumers may be queried for a social networking identifier (e.g.,handle, profile, username, etc.) such that the data gathering module 210may collect customer experience data for that consumer. When customerexperience data is obtained, the segmentation module 225 may link orcorrelate the customer experience data with primary research data, suchas data obtained from traditional marketing or consumer researchactivities. The segmentation module 225 may evaluate customer experiencedata of the consumer to determine if the consumer is acting incorrespondence with the research data gathered about the consumer.Moreover, the segmentation module 225 may also determine if the consumeris influencing other consumers with their social media conversations.The segmentation module 225 may also used the combined data sets togenerate models that allow the segmentation module 225 to predict whichsocial media conversations that should be tracked to glean the mostaccurate and relevant information about the consumer.

In other embodiments, the segmentation module 225 may utilize thecorrelated group consumers into categories based upon various factors.For example, very influential consumers who focus on superior customerservice may be clustered into a consumer segment.

The segmentation module 225 may segment or cluster the customerexperience data based upon the content of the social mediaconversations. For example, the segmentation module 225 may evaluate agroup of social media messages and determine that two thirds of theconsumer's desire superior consumer service, whereas only five percentdesire an aesthetically pleasing website. Again, the clustering, as withsentiment analysis, may be conducted based upon keywords included in thesocial data. As with PCS values and consumer experience values, thesegmentation module 225 may determine the segmentation of customerexperience data based upon certain algorithms, mathematical, and/orstatistical methodologies. According to some embodiments, thesegmentation module 225 may employ statistical methodologies such asclustering ensembles. The clustering of consumers allows the merchant todirect more resources to consumer service efforts and away from websitedevelopment. As consumer sentiments change, so may the segmentation, andthus the priorities of the merchant.

Brand Commitment Scores

Based upon the categorization of the social media conversation as beingwithin the BCS domain, the BCS module 230 may be executed to calculate aBCS for a social media conversation. According to some embodiments, theBCS quantifies brand affinity for a particular consumer or group ofconsumers. The BCS may also quantify the consumer's emotions regardingthe brand. The BCS may provide a metric, which allows merchants to buildrelationships between customers and brands.

In some embodiments, the BCS is a composite calculation that encompassesthe understand, explore, and commit segments of the brand affinityjourney. The BCS relates to the brand affinity journey inasmuch as theunderstand segment of the brand affinity journey is associated withhopefulness, the explore segment of the brand affinity journey isassociated with attraction, and the commit segment of the brand affinityjourney is associated with devotion. Keywords conveying these emotionsmay be used to categorize a social media conversation as falling withinthe brand commitment domain.

In greater detail, the hopefulness emotion attempts to quantify what isimportant to a customer. Common keywords associated with hopefulness maycomprise, but are not limited to hope, expect, optimistic, and so forth.Using this metric, merchants may be able to align expectations of theirconsumers with their brand. Merchants may tailor their branding and/ormarketing to set a level of expectation regarding their products andlong-term relationship with customers. The branding of an organizationor products may be utilized to build and maintain an emotionalconnection with consumers which may be leveraged to drive sustainedpurchasing behavior for products and advocacy in content engagement.

The attraction emotion attempts to quantify if the brand properlyreflects who their customers are. Common keywords associated withattraction may comprise, but are not limited to excited, admire, appeal,and so forth. Using this metric, merchants may be able to identifyreconciliation when needed. Merchants may tailor their branding and/ormarketing to ensure that their products are being advertised and/orbranded in accordance with the needs of their customers. These needs maycomprise reputation, quality, popularity, and so forth.

The devotion emotion attempts to quantify how deeply the consumer iscommitted to the brand. Common keywords associated with devotion maycomprise, but are not limited to love, loyal, trust, and so forth. Usingthis metric, merchants may be able to identify a relationship statusbetween a brand and a consumer. The more devoted the customer is to thebrand, the more committed the customer will be to purchasing the productassociated with the brand. Merchants may wish to tailor their brandingor marketing to drive up customer devotion and identify consumers withlagging commitment.

Because these metrics and resultant BCS may be tracked over time and perauthor, the merchant may determine how changes in marketing and/orbranding strategies affect these different consumer emotions. BCS may becalculated for groups or consumer segments such as demographic,psychographic, or other common consumer segmentations that would beknown to one of ordinary skill in the art with the present disclosurebefore them.

An exemplary algorithm (Equation A) for calculating a BCS for a socialmedia conversation is provided below:Σ(Ar/ΣAr)*Cw*Sa  (Equation A)

where an author rank score Ar is first calculated for each of a group ofauthors. The group of authors may include the known customers oralternatively, a subgroup of customers. An author rank may be calculatedby determining an influence for an author. The influence of an authormay be determined, for example, by a number of connections for theauthor (e.g., followers, contacts, etc.). The social status of an authormay also be considered. For example, an influential celebrity may havetheir conversations ranked more highly than an average consumer in someembodiments.

Once an author rank score has been calculated for each author in thegroup of authors, the author rank score for the author of the commentmay be divided by a sum of the author rank scores for each author in theauthor group to generate an adjusted author rank score. The author rankscores and/or adjusted author rank score may be calculated over a givenperiod of time, relative to a particular product. Thus, BCS may becalculated over time to provide merchants with indices or metrics thatquantify how well their branding efforts are being received byconsumers.

Next, a component weight Cw for the conversation may be multiplied withthe adjusted author rank score. The component weight may comprisepreviously established scaling factors for each stage of the productcycle. For example, the understand/hopefulness scaling factor may beapproximately 0.15, whereas the explore/attraction scaling factor may beapproximately 0.25. Additionally, the commit/devotion scaling factor maybe approximately 0.6. Thus, in some embodiments, the most importantscaling factor for component weight relative to the BCS is thecommit/devotion scaling factor. Advantageously, the commit/devotionscaling factor may be attributed more weight because the BCS attempts todetermined a brand commitment level for consumers. Therefore, devotionmay be strongly correlated to brand commitment, whereas hopefulnessand/or attraction are less likely to be indicative of brand commitment,although they may be contributory to some degree.

As mentioned previously, the component weighting for each of these threescaling factors may be determined based upon empirical evidence, such asthe evaluation of social media conversations of trustworthy authors. Forexample, a plurality of conversations gathered from various trustworthyconsumers may be utilized as the basis for setting the weight ofindividual scaling factors.

Additionally, the BCS may be weighted based upon consumer sentiment Swas described in greater detail herein. Thus, the system may calculate asentiment weight for consumers who are consistently positive, negative,neutral, or otherwise amenable to sentiment categorization. Thisconsumer sentiment Sw may be utilized to alter the BCS for a particularconversation. For example, an author who is consistently negative mayhave their BCS adjusted based upon a negative conversation. In contrast,the consistently negative author may have their BCS adjusted upwardly ifthey express devotion within their conversation for a particular brandor product, as it would be unexpected for the author to provide apositive conversation.

Customer Relevance Scores

In some embodiments, based upon the categorization of the social mediaconversation as being within the CRS domain, the CRS module 235 may beexecuted to calculate a CRS for online content. According to someembodiments, the CRS may quantify how likely it is, or will be, that acustomer or social network user will share a particular piece ofcontent, such as a video, an article, a website, or other online contentwithin the context of a social network.

For example, end users such as marketers may desire to obtainshareability metrics for online content. The data gathering module 210may be executed to gather customer experience data regarding the onlinecontent. For example, the data gathering module 210 may gather metricsregarding sharing, downloading, uploading, posting, linking,referencing, liking, tagging, or other similar actions for onlinecontent that would occur within the context of a social network. Thedata gathering module 210 may also gather conversations, commentary,blog posts, articles, or other textual information associated withonline content. For example, the data gathering module 210 may capture acomment thread associated with a video.

Next, the CRS module 235 may evaluate the customer experience data toclassify the conversations. Shareability classification may be performedby determining if keywords included in the conversations about theonline content fall within any of the columns of the CRS core matrix 315of FIG. 3. Once the social media conversations have been classified, aCRS may be calculated for each conversation, a group of conversationsfor a particular author, or of all conversations for all authors inregard to the online content in question, and so forth.

An exemplary CRS may be calculated by the CRS module 235 using EquationA provided herein. That is, the adjusted author rank score may becalculated in a similar manner described herein with regard to the BCS.In contrast with the method for calculating the BCS, the CRS maycontemplate different component weights Cw for the conversations. Thatis, the component weights for the CRS may be associated with theshareability classifications described herein such as interested,connected, and sharing. Whereas the BCS related to the understand,explore, commit product cycle segments with hopefulness, attraction, anddevotion, the CRS relates to the understand, explore, and commit productcycle segments with interested, connected, and shared.

Thus, the component weight Cw for a conversation may comprise previouslyestablished scaling factors for each stage of the product cycle. Forexample, the understand/interested scaling factor may be approximately0.15, whereas the explore/connected scaling factor may be approximately0.25. Additionally, the commit/shared scaling factor may beapproximately 0.6. Thus, the fact that the content has been shared ismore relevant to the CRS than the keywords that indicate that theconsumer is interested in the content. The connected shareabilityclassification may consider how often the consumer mentions their socialmedia connectivity and is therefore more related to shareability thanwords that simply connote an interest in the content.

As the conversations are evaluated for keywords and classified, the CRSmodule 235 may determine a shareability level for the online content.For example, if a predominate number of conversations include keywordsthat fall within the Interested classification, the CRS module 235 mayindicate that the online content is likely to pique the interests ofconsumers.

In accordance with the weightings described herein, conversations thatpredominately involve keywords in the Interested classification willgenerate lower CRS relative to conversations that predominately involvekeywords in the Connected classification in some embodiments. Therefore,conversations that predominately involve keywords in the Sharedclassification will have the highest relative CRS in some embodiments.

Again, the CRS may be calculated by the CRS module 235 at an authorlevel. Additionally, an average CRS may be calculated for a plurality ofauthors having social media conversations about selected online content.Moreover, as with the BCS, the CRS may be corrected, weighted, adjusted,or otherwise modified based upon the sentiment score for a particularauthor.

In sum, Equation A may be employed by the PCS module 215, the BCS module230, and/or the CRS module 235 to calculate the PCS, BCS, and CRS,respectively. The score calculated by the use of Equation A is dependentupon the initial classification of social media conversations as fallingwithin the PCS, BCS, or CRS domains in some embodiments. Thus, EquationA may be used to calculate PCS for social media conversations fallingwith the PCS domain, while BCS and CRS may be calculated forconversations falling within the BCS and CRS domains, respectively.

Although not shown in Equation A, the CRS for online content may beaugmented by web analytics regarding the online content. For example,the CRS module 235 may determine click counts, share counts, embeds,links, or other quantifiable ways online content may be shared withinsocial networks. These analytics may be used to adjust the CRS. Socialmedia conversations that generate relatively low CRS may be offset bythe fact that the content is, in fact, shared more frequently than theconversations imply.

To increase the shareability of online content, the code framing module240 may be utilized to generate a code frame that may be applied toonline content having a relatively low CRS. For example, a code framemay be generated to increase the CRS of online content that falls withinthe Interested classification, described herein.

The content that generated the relatively high CRS may be evaluated bythe code framing module 240 using a semiotic analysis to determinecategories that define the shareability of content. With regard tosemiotics utilized by the code framing module 240, a code or “frame” maybe built to understand how various elements of online content worktogether to create meaning and trigger subconscious sharing impulses inconsumers. These elements may be labeled as categories. Significant, butnon-limiting categories that trigger subconscious sharing impulses maycomprise a Narrative category may define the style of the content. ATheme category may define the type of story that the content isattempting to convey. An Underlying Message category may define themoral outcome or other message that the content is attempting to convey.Highly shareable content may engage the consumer on these various levelsand success in these various levels may be determined by evaluatingconsumer response to content for these various categories.

Once the code frame has been generated by the code frame module 240(also referred to as code framing module 240), online content having arelatively low CRS, which is similar to the type of content used togenerate the code frame, may be applied to the content to increase theCRS for the content.

In some instances, a semiotic analysis may be performed on the onlinecontent with the low CRS to determine the narrative, theme, andunderlying message categories for the online content. If the code framemodule 240 can determine discrepancies between the code frame and thecategories of the online content, the code frame module 240 may identifywhat categories need improvement. In other instances, marketers mayutilize the code frame as a guideline for correcting defective content(e.g., content that is not being shared adequately). According to someembodiments, the code frame may be used by marketers to generate onlinecontent from scratch. Thus, the code frame may be used as a template tocreate content that is highly likely to be shared.

According to various exemplary embodiments, the system 100 may beconfigured to generate and display customer commitment framework data.Broadly, the customer commitment framework (CCF) empowers organizationsto optimize their customer experience with a data-driven approach todecision-making. CCF provides real-time predictive measures and robustinsights to strengthen customer commitment around the three exemplarycustomer journeys, including Shopping, Sharing, and Advocacy. Withregard to Shopping, the system 100 may utilize CCF data informs andenables product and service strategy which foster awareness, feedtrends, convert shopping into customers, and insure long-term commitmentwithin target markets.

Indeed, spending money may be considered the ultimate commitmentbehavior, whether for the first time, buying more, or upgrading. The CCFdata allows users to make marketing investments that lead customersalong the product journey and closer to purchase through understandingthe enablers and barriers to purchase. Responsive, intelligent marketingcan influence the drivers behind purchasing. To support the journey andclosing of shopping behaviors, the entire enterprise must be alignedwith effective sales channels and supply chain to create the optimalcustomer experience. CCF data may be utilized by the system 100 toprovide the user with visually appropriate and intuitive dashboards thatpresent metrics (e.g., scores) in a manner that allows the user todetermine instances where the closing of shopping behaviors may beincreased.

With regard to Sharing, the system 100 may utilize and display CCF data,allowing the user to develop a content strategy for creating impactfuland relevant content, leading to highly engaged customers.Advantageously, these engaged customers are more likely to share andamplify the impact of advertising content.

Indeed, effective content production and management are critical toensure that messages and customer experience across all channels arerelevant. Relevancy drives sharing and the CCF data generated anddisplayed by the system 100 can increase the likelihood of userendorsement of products and services.

With regard to Advocacy, the system 100 may utilize CCF data to informusers of their brand strategies, which may be used to create vocaladvocates for products and services. In some embodiments, the system 100may generate Customer Commitment Dashboards (CCDs), provide socialdata-derived KPIs and journey mapping, as well as customer segmentation.When coupled with human-led analysis and consulting, this system createsa deep and real-time understanding of your customer's experiences alongtheir journey; as well as targets your organizational investments onwhat is most important to your most valuable customers.

It will be understood that the CCDs generated by the present technologymay effectively model customer experience data, and provide product andbrand scores, which may be benchmarked and compared to competitors, aswell as an industry average, providing the ability to leverage bestpractices in any given industry vertical.

Generally, the system 100 may analyze CCF data to evaluate a customerjourney with predictive scores that provide an understanding of thebarriers and enablers in the customer journey to shopping, advocacy andsharing. Additionally, the system 100 may model social data to customerchallenges and opportunities in real-time, enabling users to plan andreact quickly, by providing predictive measures with appropriatediagnostics.

Additionally, the CCF data generated by the system 100 may be used topinpoint both challenges and opportunities for enhancing the customerexperience by applying CCF scores (PCS, CRS, BCS) to specific stages ofthe customer journey, suggesting which areas of an organization shouldbe engage to increase customer response. In addition, in someembodiments the system 100 selectively targets CCF data and analyzesonly those social media conversations that are indicative of a keycustomer journey, turning the “big” social dataset into targetedinsights for effective course correction via the use of dashboards.Again, the system 100 may also model CCF data for brand and productsagainst competitors CCF data to locate areas of competitive opportunityand highlight a competitor's best practices.

In some instances, the system 100 may provide statistical links betweenCCF scores (such as PCS, CRS, and BCS) and established scorecard metricssuch as customer satisfaction (CSAT), net satisfaction score (NSAT), andnet promoter score (NPS)—just to name a few. Such comparisons allowusers to understand how these measures move together and impact eachother to enable smarter decision-making and corporate scorecardimprovement.

According to some embodiments, the system 100 may be programmed toobtain customer experience data from one or more social media platformsregarding a first entity, such as the user who desires to view CCDsregarding their CCF data. In some instances, periodic calculations ofcustomer commitment framework data from the customer experience data areperformed. As mentioned above, this CCF data may include, but is notlimited to the PCS, BCS, and CRS scores described above, but may alsoinclude ancillary or complementary scores such as CSAT and the like. Insome instances, the system 100 may calculate these scores at a giveninterval, such as every hour, or each day. These scores may be stored ina database (such as database 120 of FIG. 1), also referred to as a datalaboratory.

Upon a user request, the system 100 may generate a customer commitmentdashboard that comprises a graphical representation of the customercommitment framework data. While various types of representations ofcustomer commitment framework data will be described in greater detailbelow, it will be mentioned that CCF data may be calculated for aplurality of market segments and in a plurality of languages. Thus,users that market their products in various markets, and to consumers invarious countries, may use CCF data that may be used to evaluate andimprove marketing within these markets and languages.

In some instances, the system 100 may calculate average scores for aplurality of industry verticals. It is noteworthy that an average scoremay include any of an average product commitment score, an average brandcommitment score, and an average customer relevance score. These“average” scores represent an average of corresponding scores forcompetitors of the first entity. Thus, a user can compare their dataagainst an aggregate/average score relative to their competition.

Generally, FIGS. 4-12 each illustrate exemplary graphical userinterfaces in the form of customer commitment dashboards. Thesedashboards are merely exemplary and are representative of the many typesof GUIs that may be generated by the system 100. Thus, one of ordinaryskill in the art will appreciate that exact types of CCF data, and theirarrangement into a CCD may depend upon the type of information that isrelevant to the viewer. The system 100 may utilize benchmarkingcomparisons as the basis for determining how the data is displayed tothe user, or when data is displayed to the end user. For example, if thesystem 100 detects highly discrepant PCS score(s), relative to anaverage PCS score for a company's nearest competitors, the system 100may choose to display such data first, or highlight such data to drawthe attention of the user.

FIG. 4 is an exemplary customer commitment dashboard illustratingproduct commitment score data. The dashboard is shown as comprising PCSquadrant graph 505, which includes data points for a plurality ofcompeting products. The graph 505 is a function of PCS score values,which extend across the Y-axis of the graph, while product volume valuesextend across the X-axis of the graph. In this instances, the PCS numbercomprise year-to-date PCS scores averages for each product.

The dashboard also includes a table representation 510 showing industryvertical CCF data, which includes average PCS scores for each vertical,a highest PCS score for each vertical, as well as a lowest PCS score foreach vertical.

FIG. 5 is an exemplary customer commitment dashboard illustratingproduct commitment score data as well as brand commitment score data.The dashboard of FIG. 5 includes the same data as provided in FIG. 4,and additionally includes similar graphs and tables for BCS scores. FIG.6 is an exemplary customer commitment dashboard illustrating productcommitment score data as well as brand commitment score data andcustomer relevancy score data.

FIG. 7 is an exemplary customer commitment dashboard illustratingproduct commitment score (PCS) data displayed according to segmentation.More specifically, the dashboard includes a graphical representation 705of PCS scores as a function of influence, where the Y-axis includes arange of influence and the X-axis is includes PCS scores which increasefrom left to right. Thus, the CCF data for a product or service may bevisually represented, graphically, according to customer segments,showing the influence of a product or a marketing campaign'seffectiveness. A summary paragraph may be provided that describes eachparticular segment shown in the graph 705. A control panel 710 providescontrols which allow the user to select temporal ranges over which datamay be viewed. For example, users may select to view PCS scores forsegments over a period of a week, a month, a quarter, or a year, or mayutilize a pair of date input boxes to provide a bounded range of dates.

FIG. 8 is an exemplary customer commitment dashboard illustrating PCSsegmentation and customer journey data. The dashboard includes atable-based representation 805 of segment data, broken down into journeyphases. More specifically, each row of the table includes a uniquejourney phase of a product. Each of the columns of the table include aspecific segment. The cells of the table include PCS scores for eachjourney phase of each segment. It will be understood that while thecells are described as including PCS scores, the cells may be filledalternatively with BCS scores or CRS scores, depending on the CCF dataselected by the user. The user may toggle through CCF data scores (e.g.,PCS, BCS, and CRS using PCS tab 810 a, BCS tab 810 b, and CRS tab 810 c,respectively).

FIG. 9 is an exemplary customer commitment dashboard illustrating PCSsegmentation and segmentation characteristics. The dashboard of FIG. 9includes a table-based representation 905 of CCF data, where eachsegment is displayed in row format, while segment characteristics aredisplayed in column format. Exemplary segment characteristics includeinfluence, PCS score, experience score, and experience change scores.The experience change scores may assist a user in identifying net scorechanges over time.

FIG. 10 is an exemplary customer commitment dashboard illustrating atime-based and graphical PCS data analysis. The dashboard of FIG. 10includes a graphical representation 1005 of PCS scores for variousproducts. PCS scores on the Y-axis are shown over time on the X-axis. Aband for highest and lowest PCS scores is overlaid onto the graph, aswell as individual PCS scores for the first entity's product, which inthis case includes SM2. A trend line is also overlaid onto the graph.Similar graphical representations for competitive position 1010, anddriver performance over time 1015 are also shown.

FIG. 11 is an exemplary customer commitment dashboard illustrating atime-based and graphical analysis of customer journey data. Thisdashboard includes a graphical representation 1105 of experience scoresversus average experience scores within each phase of a customerjourney. Exemplary phases include product awareness, connection,evaluation, through to long term commitment. A table representation ofexperience scores 1110 for a plurality of products are provided, as wellas a market level analysis of experience scores 1115.

FIG. 12 is an exemplary customer commitment dashboard illustrating atime-based and graphical analysis of experience scores relative toproduct awareness. This dashboard includes similar CCF data relative tothe dashboard of FIG. 11, with an emphasis placed on scores over time.

FIG. 13 illustrates an exemplary computing system 1300 that may be usedto implement an embodiment of the present technology. The system 1300 ofFIG. 13 may be implemented in the contexts of the likes of computingsystems, networks, servers, or combinations thereof disclosed herein.The computing system 1300 of FIG. 13 includes one or more processors1310 and main memory 1320. Main memory 1320 stores, in part,instructions and data for execution by processor 1310. Main memory 1320may store the executable code when in operation. The system 1300 of FIG.13 further includes a mass storage device 1330, portable storage mediumdrive(s) 1340, output devices 1350, user input devices 1360, a graphicsdisplay 1370, and peripheral devices 1380.

The components shown in FIG. 13 are depicted as being connected via asingle bus 1390. The components may be connected through one or moredata transport means. Processor unit 1310 and main memory 1320 may beconnected via a local microprocessor bus, and the mass storage device1330, peripheral device(s) 1380, portable storage device 1340, anddisplay system 1370 may be connected via one or more input/output (I/O)buses.

Mass storage device 1330, which may be implemented with a magnetic diskdrive or an optical disk drive, is a non-volatile storage device forstoring data and instructions for use by processor unit 1310. Massstorage device 1330 may store the system software for implementingembodiments of the present technology for purposes of loading thatsoftware into main memory 1320.

Portable storage device 1340 operates in conjunction with a portablenon-volatile storage medium, such as a floppy disk, compact disk,digital video disc, or USB storage device, to input and output data andcode to and from the computer system 1300 of FIG. 13. The systemsoftware for implementing embodiments of the present technology may bestored on such a portable medium and input to the computer system 1300via the portable storage device 1340.

Input devices 1360 provide a portion of a user interface. Input devices1360 may include an alphanumeric keypad, such as a keyboard, forinputting alpha-numeric and other information, or a pointing device,such as a mouse, a trackball, stylus, or cursor direction keys.Additionally, the system 1300 as shown in FIG. 13 includes outputdevices 1350. Suitable output devices include speakers, printers,network interfaces, and monitors.

Display system 1370 may include a liquid crystal display (LCD) or othersuitable display device. Display system 1370 receives textual andgraphical information, and processes the information for output to thedisplay device.

Peripherals 1380 may include any type of computer support device to addadditional functionality to the computer system. Peripheral device(s)1380 may include a modem or a router.

The components provided in the computer system 1300 of FIG. 13 are thosetypically found in computer systems that may be suitable for use withembodiments of the present technology and are intended to represent abroad category of such computer components that are well known in theart. Thus, the computer system 1300 of FIG. 13 may be a personalcomputer, hand held computing system, telephone, mobile computingsystem, workstation, server, minicomputer, mainframe computer, or anyother computing system. The computer may also include different busconfigurations, networked platforms, multi-processor platforms, etc.Various operating systems may be used including Unix, Linux, Windows,Macintosh OS, Palm OS, Android, iPhone OS and other suitable operatingsystems.

It is noteworthy that any hardware platform suitable for performing theprocessing described herein is suitable for use with the technology.Computer-readable storage media refer to any medium or media thatparticipate in providing instructions to a central processing unit(CPU), a processor, a microcontroller, or the like. Such media may takeforms including, but not limited to, non-volatile and volatile mediasuch as optical or magnetic disks and dynamic memory, respectively.Common forms of computer-readable storage media include a floppy disk, aflexible disk, a hard disk, magnetic tape, any other magnetic storagemedium, a CD-ROM disk, digital video disk (DVD), any other opticalstorage medium, RAM, PROM, EPROM, a FLASHEPROM, and any other memorychip or cartridge.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. The descriptions are not intended to limit the scope of thetechnology to the particular forms set forth herein. Thus, the breadthand scope of a preferred embodiment should not be limited by any of theabove-described exemplary embodiments. It should be understood that theabove description is illustrative and not restrictive. To the contrary,the present descriptions are intended to cover such alternatives,modifications, and equivalents as may be included within the spirit andscope of the technology as defined by the appended claims and otherwiseappreciated by one of ordinary skill in the art. The scope of thetechnology should, therefore, be determined not with reference to theabove description, but instead should be determined with reference tothe appended claims along with their full scope of equivalents.

What is claimed is:
 1. A method, comprising: obtaining, via a digitalintelligence system, customer experience data regarding any of aproduct, a brand, and customer relevance for a first entity;periodically calculating, via the digital intelligence system, customercommitment framework data from the customer experience data; calculatingan average score for a plurality of industry verticals, wherein theaverage score includes any of an average product commitment score, anaverage brand commitment score, and an average customer relevance score,which represents an average of scores for a plurality of entities withinan industry vertical; and generating a customer commitment dashboardthat comprises a graphical representation of the customer commitmentframework data.
 2. The method according to claim 1, wherein the customercommitment framework data comprises any of a product commitment score, abrand commitment score, and a customer relevance score.
 3. The methodaccording to claim 2, wherein the product commitment score, the brandcommitment score, and the customer relevance score are calculated for aplurality of market segments.
 4. The method according to claim 2,wherein the product commitment score, the brand commitment score, andthe customer relevance score are calculated for a plurality oflanguages.
 5. The method according to claim 1, wherein the customercommitment dashboard comprises a corresponding graphical representationfor customer experience data regarding any of a product, a brand, andcustomer relevance for a second entity.
 6. The method according to claim5, wherein the second entity is a competitor of the first entity and thecorresponding graphical representation of customer experience data forthe second entity is utilized as a benchmark.
 7. The method according toclaim 1, further comprising generating a graphical representation ofsegmentation that is a function of an influence vertical and any ofproduct commitment score, brand commitment score, and customer relevancescore horizontal.
 8. A system, comprising: one or more processors; andlogic encoded in one or more tangible media for execution by the one ormore processors and when executed operable to perform operationscomprising: obtaining, via a digital intelligence system, customerexperience data regarding any of a product, a brand, and customerrelevance for a first entity; periodically calculating, via the digitalintelligence system, customer commitment framework data from thecustomer experience data; and generating a customer commitment dashboardthat comprises a graphical representation of the customer commitmentframework data, wherein the graphical representation comprises a tableof segmentation for any of a product, a brand, and customer relevance,wherein journey phases are displayed in rows and segments are displayedin columns, wherein cells of the table include at least one of a productcommitment score, a brand commitment score, and a customer relevancescore.
 9. The system according to claim 8, wherein the logic whenexecuted is further operable to perform operations comprising generatinga table of segment characteristics for one or more of the segmentations.10. The system according to claim 8, wherein the logic when executed isfurther operable to perform operations comprising generating a graphicalrepresentation of any of product commitment scores, brand commitmentscores, and customer relevance scores for the first entity, over aperiod of time.
 11. The system according to claim 10, wherein the logicwhen executed is further operable to perform operations comprisingoverlaying any of product commitment scores, brand commitment scores,and customer relevance scores for a plurality of competitors onto thegraphical representation.
 12. The system according to claim 10, whereinthe logic when executed is further operable to perform operationscomprising overlaying product commitment scores for a plurality ofcompeting products onto the graphical representation in such a way thatthe product commitment scores, brand commitment scores, and customerrelevance scores of the first entity are distinguishable from theproduct commitment scores, brand commitment scores, and customerrelevance scores of the plurality of competing products.
 13. The systemaccording to claim 8, wherein the customer commitment dashboardcomprises a link that exposes customer commitment framework datautilized to generate the graphical representation of the customercommitment framework data.
 14. The system according to claim 8, whereinthe customer commitment framework data comprises any of a productcommitment score, a brand commitment score, and a customer relevancescore.
 15. The system according to claim 14, wherein the productcommitment score, the brand commitment score, and the customer relevancescore are calculated for any of a plurality of market segments and aplurality of languages.
 16. A system, comprising: one or moreprocessors; and logic encoded in one or more tangible media forexecution by the one or more processors and when executed operable toperform operations comprising: obtaining, via a digital intelligencesystem, customer experience data regarding any of a product, a brand,and customer relevance for a first entity; periodically calculating, viathe digital intelligence system, customer commitment framework data fromthe customer experience data; and generating a customer commitmentdashboard that comprises a graphical representation of the customercommitment framework data; and wherein the logic when executed isfurther operable to perform operations comprising generating a table ofsegmentation for any of a product, a brand, and customer relevance,wherein journey phases are displayed in rows and segments are displayedin columns, wherein cells of the table include at least one of a productcommitment score, a brand commitment score, and a customer relevancescore.
 17. A system, comprising: one or more processors; and logicencoded in one or more tangible media for execution by the one or moreprocessors and when executed operable to perform operations comprising:obtaining, via a digital intelligence system, customer experience dataregarding any of a product, a brand, and customer relevance for a firstentity; periodically calculating, via the digital intelligence system,customer commitment framework data from the customer experience data;and generating a customer commitment dashboard that comprises agraphical representation of the customer commitment framework data;wherein the logic when executed is further operable to performoperations comprising generating a table of segmentation for any of aproduct, a brand, and customer relevance, wherein journey phases aredisplayed in rows and segments are displayed in columns, and whereincells of the table include at least one of a product commitment score, abrand commitment score, and a customer relevance score.
 18. The systemaccording to claim 17, wherein the logic when executed is furtheroperable to perform operations comprising generating a table of segmentcharacteristics for one or more of the segmentations.
 19. The systemaccording to claim 17, wherein the logic when executed is furtheroperable to perform operations comprising generating a graphicalrepresentation of any of product commitment scores, brand commitmentscores, and customer relevance scores for the first entity, over aperiod of time.